By Joel Kahil, Benn Glorfield, and Eli Barbieri.

Liquid Staking: the New Crypto Narrative

The long-awaited Shapella upgrade, expected to go live in early spring, will enable validators to exit. The upgrade is set to increase the number of ETH validators going forward, as staking will become safer, and validators will no longer have to lock their capital for an undetermined amount of time. The anticipated changes have given rise to a new crypto narrative around liquid staking — strong overall growth in the sector, with opportunities for new entrants to challenge Lido’s and CEXes’ domination. The staking narrative is built around:

• Low staking ratio on Ethereum. Only 14% of total ETH is currently staked, which is far below that of other chains like Polygon and Solana.
• Yield opportunities. Staking yields are currently between 4–5%. Our previous research article, analyzed the wallet activity of the top 1,000 ETH holders. We noticed that a large proportion of these (~20% of total ETH supply) would benefit from staking ETH due to having idle ETH balances and little spending needs. This is a strong potential driver for staking in the upcoming months.
• Possibility of increased incentives. Allowing withdrawals in the Shapella upgrade enables the churn between liquid staking providers. This should reduce barriers to entry, encouraging protocols to increase incentives and attract more stakers.
• The integration of liquid staking tokens (such as stETH, rETH) into DeFi protocols should accelerate post-Shapella. Liquid staking tokens are more composable, improving capital efficiency and returns for users.

According to DefiLlama, liquid staking is currently the second largest DeFi category in terms of total value locked (TVL), with $14 billion in assets, the majority of which are staked ETH. Dominating this market could prove to be quite lucrative and a number of new projects have recently launched and are set to compete with the already established protocols. 2023 also saw the introduction of the first staking ETP (Exchange Traded Product) by 21Shares and [on-chain index](https://indexcoop.com/dseth-diversified-staked-eth-index.) by Index Coop. Assets under management remain negligible, with only $100k for 21Shares and $700k for Index Coop. While the development of institutional-grade staking capabilities is promising, whether these index products will take off remains to be seen.

Regulatory Tailwind

Liquid Staking has benefited from the recent SEC crackdown on centralized exchange (CEX) staking. In February, the SEC deemed Kraken to be operating an “unlawful offer and sale of securities.” Kraken’s staking program was criticized for commingling staking assets with Kraken’s own assets, offering stable investment returns that were unrelated to protocol-level staking returns, a lack of transparency, and marketing its services to retail investors. As a result, Kraken paid a $30m fine and announced that it would unwind its US staking operations. Although this situation currently only affects US customers, this has brought some regulatory uncertainty for other CEX staking programs, particularly Coinbase and its cbETH, which accounts for a little over 2M+ staked ETH. Since almost a quarter of the total ETH staked comes from CEXes, monitoring what Kraken’s US clients will decide to do once the staking program unwinds could reveal the direction the staking market is headed. Will they move to another exchange? Will they move their assets into liquid staking?

Many see the current regulatory crackdown as a boost for liquid staking tokens, as it reduces the influence of their main competitor. Some argue that liquid staking should be protected from regulatory oversight due to its decentralized nature. The Proof of Stake Alliance, co-chaired by Lido Finance and other industry experts, commissioned two external whitepapers which suggested that:

1. The term liquid staking derivatives is misleading and that “liquid staking tokens” is more appropriate.
2. Liquid staking tokens should not be considered as investment securities.
3. The conversion of crypto assets for receipt tokens should not be considered a taxable transaction for U.S. federal income tax purposes.

Strong Price Performance YTD

Tokens related to liquid staking have all shown substantial gains year-to-date, ranging from +136% for Rocket Pool to +316% for SSV.Network. While not strictly a liquid staking protocol, SSV.Network is one of the main operators of Distributed Validator Technology (DVT) along with Obol. Liquid staking protocols hope to integrate DVT after the Shapella upgrade to become truly decentralized.

Liquid Staking Gaining Market Share YTD.

The strong price performance of the asset is supported by gains in market share and inflows into liquid staking. In the first two months of 2023, an additional 1.55 million ETH was staked, which is approximately 10% of the total amount staked at the end of 2022. Of those ETH, 60% went to liquid staking protocols, while only 4% of inflows went to CEXes. This indicates that CEXes are no longer seen as attractive by stakers.

When it comes to protocols, Lido has been the clear winner, attracting 51% of total staking in the first two months of 2023. On February 25th, 2023, it registered its largest daily stake inflow with over 150k ETH staked. Far behind, Rocket Pool only attracted 5.2% of deposits and Frax Finance, 3.1%. It is unlikely that Lido’s competitors will catch up, as Lido benefits from economies of scale. Higher TVL means more resources to spend on integrations and development, which also means higher liquidity and DeFi composability.

Rocket Pool and Lido are preparing for the Post Shapella

As the Shapella upgrade approaches, liquid staking protocols have increased communication efforts in February to attract stakers and investors.

Rocket Pool

• Coinbase has made a significant investment in Rocket Pool. This investment will provide Coinbase with considerable influence over the oDAO proposals. Additionally, Coinbase will list a market on its exchange for RPL-rETH, improving liquidity.
• Rocket Pool is preparing for the Atlas Upgrade, which will reduce the requirement to run mini pools from 16 ETH to 8 ETH. This change aims to allow more people to run their own minipool. Currently, there are not enough validators to run minipools, causing rETH to trade at a premium to its fair market value.
• nodeset.io presented their new solution, “Hyperdrive”, which focuses on improving the scalability of Rocket Pool. The plan is to create a secondary system on top of Rocket Pool that eliminates the bond requirement by distributing deposits to node operators in a fully non-custodial manner. The system relies on an administrator who adjusts parameters, such as commission rates and deposit pool size, based on the reputation and good behavior of each node operator. The administrators also have the power to eject underperforming node operators. Hyperdrive is expected to launch in 2023.

Lido

• Lido was the first liquid staking protocol to unveil a detailed framework on how the withdrawals will be implemented.
• Lido is also preparing for the future decentralization of the protocol by unveiling its new Staking Router. The router makes the existing smart contract more modular by allowing various validator subsets (solo stakers, distributed validator technology, or permissionless staking).
• Lido also plans to maintain its dominance by focusing efforts on L2s (Arbitrum and Optimism). This is a smart strategic move since the number of transactions on L2 has recently surpassed that on Ethereum. Lido was awarded 1m OP tokens to incentivize wstETH adoption, which is mostly used in DeFi. These rewards will be distributed to liquidity providers on DEXes and lending protocols such as Aave.

Nethermind is preparing to publish an in-depth research paper on the future of liquid staking. The paper will examine the differences between various types of projects and how we see technology evolving in the future.

Disclaimer

This article has been prepared for the general information and understanding of the readers. No representation or warranty, express or implied, is given by Nethermind as to the accuracy or completeness of the information or opinions contained in the above article.

The views and strategies described may not be suitable for all readers. Both past performance and yield may not be a reliable guide to future performance. All case studies are shown for illustrative purposes only and should not be relied upon as advice or interpreted as a recommendation.

To the fullest extent permitted by law, Nethermind disclaims any liability in connection with this article, its content, and any related services and products and your use thereof, including, without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. Nethermind does not warrant, endorse, guarantee, or assume responsibility for any product or service advertised or offered by a third party through the product, any open source or third-party software, code, libraries, materials, or information linked to, called by, referenced by or accessible through the article, its content, and the related services and products, any hyperlinked websites, any websites or mobile applications appearing on any advertising, and Nethermind will not be a party to or in any way be responsible for monitoring any transaction between you and any third-party. You should use your best judgment and exercise caution where appropriate. FOR AVOIDANCE OF DOUBT, THE ARTICLE, ITS CONTENT, ACCESS, AND/OR USAGE THEREOF, INCLUDING ANY ASSOCIATED SERVICES OR MATERIALS, SHALL NOT BE CONSIDERED OR RELIED UPON AS ANY FORM OF FINANCIAL, INVESTMENT, TAX, LEGAL, REGULATORY, OR OTHER ADVICE.

Kindly note, Nethermind is currently engaged in a research project for Lido DAO. This article, however, has not been commissioned by Lido DAO, and has been prepared outside of the scope of the services provided to the DAO by Nethermind.

The Liquid Staking Rush was originally published in Nethermind.eth on Medium, where people are continuing the conversation by highlighting and responding to this story.

By Basat Gorchu, Harshvardhan Todi, Joel Kahil, Narut Pramojaney

Executive Summary

There are concerns about large validator exits occurring shortly after the Shapella upgrade. In this article, we examine whether these fears are justified and attempt to estimate how many validators are expected to exit and how many will join post-upgrade.

First, we explain the distinction between partial and complete withdrawals, noting that not all validators can unstake immediately after withdrawals are enabled. We then examine the various drivers of withdrawal and staking demand, such as:

• Are early validators more likely to unstake?
• Is there a relationship between ETH prices and staking demand?
• Will validators restake their accumulated rewards?
• How great is the pent-up demand for staking?

Exits vs. Partial Withdrawals

The Shapella upgrade will introduce two distinct types of withdrawals: full (also known as exits) and partial (staking reward collection). For more detailed information on the withdrawal process, we refer you to our recent article in collaboration with Twinstake.

This section will focus on the expected ETH outflow associated with full and partial withdrawals.

Full Withdrawals or Exits

Complete withdrawals allow validators to withdraw their entire stake after going through an exit queue and a withdrawal period. This process was designed to prevent sudden changes in the number of validators, ensuring network security.

How Does The Exit Queue Work?

Full withdrawals must go through both the exit queue and the withdrawal period to be considered withdrawable. The exit queue is the main bottleneck and works in a first-in, first-out (FIFO) fashion, limiting the number of validators who can withdraw in each epoch (period of 32 slots) to a dynamic churn limit. The following equation determines the limit:

The minimum churn limit is currently set to four, meaning that at least four validators must be able to withdraw every epoch. The graph below shows the relationship between the number of active validators in the network and the churn limit.

Given that there are close to 530,000 validators as of February 27th, 2023, the churn limit is 530,000 / 65,536 = 8.08, which means that only eight validators are allowed to exit per epoch (6.4 minutes). Once the number of validators exceeds 589,824 (65,536*89), nine validators will be allowed to exit.

The chart below shows the days for a given proportion of the validator set to exit the queue altogether. If 10% of validators decided to unstake simultaneously, they would have to wait in the queue for 16 days on average.

Once validators go through the exit queue, they must wait for the withdrawal period to end. The withdrawal period will be 256 epochs (~27 hours) for validators who have not been slashed and 8,192 epochs (~36 days) for those who have. Once the withdrawal period is over, the validators will wait for their withdrawal to be processed (potentially up to 5 days).

Slashed actors have a longer withdrawal period due to the slashing penalty mechanism. Once slashing is detected, the minimum penalty is issued, which has a fixed size. After 18 days, a midterm attack multiplier penalty is applied, proportional to the number of other slashings in approximately the previous 36 days.

Please refer to our previous article for more information on the exit queue.

Only 40% of Validators Will be Eligible to Withdraw From Day One

It is also important to note that to be able to withdraw their stake, validators will need a withdrawal address of the 0x01 format. The option of setting a 0x01 withdrawal address when joining the validator set was only introduced in March 2021. However, the validators that joined earlier were stuck with the old 0x00 format addresses without the option of switching to the new format. Currently around ~310,000 or ~58% of Ethereum validators have the old 0x00 format withdrawal addresses. The Shapella upgrade will enable those with the old format withdrawal addresses to switch to the eligible 0x01 addresses. However, the rate of withdrawal address switching will be limited to 16 per block, meaning it would take around three days for all 0x00 addresses to change to 0x01 and become eligible to withdraw their ETH.

Partial Withdrawals

After the Shapella upgrade, accumulated staking rewards (balances over 32 ETH) will be withdrawn automatically if validators have the required withdrawal credentials. This is referred to as partial withdrawals. Partial withdrawals are processed according to the indices assigned to individual validators when joining the Beacon Chain (as opposed to first-in, first-out), with a withdrawal pointer looping through these indices and withdrawing at a rate of 16 validators per block. As a result, it would take 4.7 days for the pointer to go through all the validator indices, assuming they are all eligible. However, since only 42% of validators have the required credentials, partial withdrawals will be processed within approximately 2 days.

Drivers for Withdrawal and Staking Demand

Having covered the withdrawal process and eligibility criteria for validators, we can delve into the factors that drive demand for staking withdrawals and activations. Several factors may influence the relationship between the two, including:

1. Validators’ staking duration
2. Regulatory crackdown on CEX staking
3. Crypto prices
4. Restaking of previously accumulated rewards
5. Amount of idle ETH

Validator Staking Duration

It is commonly believed that early validators will be especially eager to use the Shapella upgrade to unstake. Currently, a significant portion of active validators, ~56.9%, have been staking for over a year, so if the aforementioned belief holds true, the majority of the validator set may wish to exit once the upgrade is live. To test this hypothesis, we analyzed withdrawals on Polygon and drew comparisons.

Insights From Polygon Staking

Insights from Polygon staking contradict the common belief that validators who have staked for a prolonged period are more likely to withdraw.

Withdrawals on Polygon have been enabled since the inception of the staking contract in 2020. According to Dune Analytics, 36,101 wallets have staked MATIC on Polygon. Of those, 55% have only withdrawn rewards and not their entire stake. For those who did unstake, most did so within the first three months. The median staking time was 92 days.

Small-stake validators on Polygon are less likely to exit, with only 42% of them having done so. Large MATIC stakers, holding 10 million or more tokens, are also relatively unlikely to exit. However, we note that middle-sized validators are more likely to unstake. This suggests that large Ethereum holders will likely retain their staked ETH, while those with smaller holdings may be more inclined to exit.

Based on these findings, we do not anticipate validators that have been staking ETH for over a year to exit, especially if they are associated with entities that control many validators.

Regulatory crackdown on CEX staking

The chart below provides a breakdown of staking deposits by user type. CEXs (note that Coinbase’s cbETH is classified as a CEX and not liquid staking) make up 28% of the total ETH staked, but they are a volatile source. CEXs may be more likely to withdraw their staked ETH since Kraken was recently fined by the SEC for the illegal sale of securities. Kraken, in particular, will unwind its US staking operations after the Shapella upgrade. Based on the SEC filing, we estimate that between 25% to 30% of Kraken’s staked ETH is from US investors, which represents between 300k to 400k ETH (2% of total staked ETH) that will be unstaked.

It is yet to be seen whether Kraken will exit its validators all at once or gradually over several weeks. Regardless of the approach, the withdrawals are expected to be manageable and not cause significant congestion to the exit queue. Nonetheless, monitoring other SEC actions against US CEXs in the coming weeks is essential, as they could destabilize the staking market and lead to large exit queues.

Impact of Crypto Prices on Staking Demand

Crypto price volatility is often seen as a critical factor in redemptions. Analysis of MATIC redemptions reveals a non-linear correlation between MATIC volatility and the amount of unstaking. The probability of a large unstaking of MATIC over 10 days (i.e. more than 2% of total staked MATIC) increases significantly when MATIC has a 15%+ drawdown. Historically, when MATIC has dropped more than 15% over 10 days, there has been a 41.8% probability of a large unstake vs 4.3% otherwise.

We observed a similar pattern with Solana staking as the main unstaking waves occurred during the 2021 flash crash and FTX collapse.

Inversely, sustained token price appreciation does not lead to increased staking. The amount of MATIC staked nearly doubled from 2022, despite the price of MATIC decreasing by a factor 3. Similarly, the amount of ETH staked increased moderately between July and December 2021, while the price multiplied by 2.5x.

Rewards Accumulated and Restaking Demand

How Large Are The Rewards Accumulated by Validators?

According to beaconcha.in, there are ~530,000 active validators, with an average balance of 34 ETH. Since anything over 32 ETH can be considered staking rewards and will be automatically withdrawn through partial withdrawals, approximately 1,060,000 ETH or 6.2% of the total staked ETH will be available for partial withdrawals.

However, as we saw earlier, most validators have yet to switch to 0x01 addresses. Assuming all remaining validators switch credentials once the Shapella upgrade is live, the highest volumes of partial withdrawals will only occur two days after the upgrade. As a result, most of the staking rewards will take two to three days to be withdrawn.

Restaking Demand May Clog The Activation Queue

Since validators don’t receive any additional yield for staking more than 32 ETH, a large portion of the partially withdrawn ETH will likely be restaked in new 32 ETH units that generate extra yield. The queue mechanics for activations (becoming a validator) are similar to those for withdrawals — only a limited number of validators can join the validator set each epoch. For example, if all 1,060,000 ETH are restaked, a validator could take up to 20 days to join the network.

While waiting in the activation queue, validators don’t generate any staking rewards. This may lead to a more significant premium for liquid staking derivatives, which will continue to generate yield while ETH in the activation queue doesn’t. Based on 5% staking returns, a 20-day activation wait would warrant a 27 basis points premium (5%/365*20). The activation queue may be longer if the Shapella upgrade encourages ETH holders to stake more.

How Large is The Pent-Up Demand For Staking?

The primary demand for staking will come from the idle ETH in wallets. Staking yields are close to 5%, meaning that not staking incurs a high opportunity cost. Therefore, holders have a solid incentive to stake their idle ETH, provided gas fees are reasonable, and they have no security or withdrawal worries. Below we examine the primary uses of the top 1,000 ETH holders. These holders, including the Beacon Chain, control 62.54% of the total ETH in circulation.

Aside from staking, a significant portion of ETH is held by individual wallets. After analyzing transactions, we can divide these wallets into three distinct groups:

1. Wallets with low turnover (10 transactions or less): 14.34% of total ETH supply.
2. Wallets accumulating ETH. These wallets have few outgoing transactions (less than 5): 7.24% of the total ETH supply.
3. Remaining wallets. These wallets have a high turnover rate: 7.88% of the total ETH supply.

The first two groups of wallets, those with low turnover or those accumulating ETH, likely have little need to spend it in the short term. Therefore, they would benefit from staking their ETH to earn interest. Together, these two categories represent 21.58% of total ETH holdings, which is higher than the amount currently staked.

In addition to these categories, treasuries are prime targets for staking, as they collectively own over 1% of the total ETH supply. Examples of DAOs in this category include ENS, BitDAO, GuildFi, and Lido, which have amassed a war chest and plan to spend it over time. These treasuries could stake the majority of their ETH to improve their treasury management.

CEXs account for approximately 10% of the total circulating ETH, while DeFi protocols account for around 5%. We anticipate that ETH will remain on those platforms, as the need to stake ETH is less pressing than for idle wallets.

We have observed that the Top 1000 ETH holders are likely to generate significant staking demand. Although we have not yet analyzed the remaining ETH wallets (which account for 37% of the total ETH), we anticipate that they, too, will contribute to the demand for staking. Smaller wallets may opt for liquid staking as a more cost-effective solution.

Conclusion

Although predicting the exact number of withdrawals post-Shapella is challenging, we expect only a limited number to exit immediately after withdrawals are enabled, as most validators will not have the necessary withdrawal credentials. The week following the Shapella upgrade should see substantial partial withdrawals, providing a good cushion for validators needing liquidity without requiring them to fully withdraw . Some partial withdrawals will be reinvested, but it will take some time for all the new validators to join the network and earn rewards due to the effect of the activation queue. We also anticipate increased demand from long-term ETH holders with idle ETH in their wallets. This could cause liquid staking coins, such as stETH, to trade at a premium compared to their fair value. For long term projections, past staking activity on Polygon and Solana suggests that validator exits are usually relatively small, barring a significant liquidity event. Nevertheless, the SEC’s recent crackdown on Kraken, which will force it to unwind its US staking operations, creates major uncertainty in the industry. Therefore, it is necessary to monitor how the SEC’s actions develop and whether they expand their reach to other cryptocurrency exchanges (CEXs), forcing more withdrawals to follow.

About Us

Nethermind is a team of world-class builders and researchers. We empower enterprises and developers worldwide to access and build upon the decentralized web. Our work touches every part of the Web3 ecosystem, from our Nethermind node to fundamental cryptography research and application-layer protocol development. We’re always looking for passionate people to join us in solving Ethereum’s most difficult challenges. Are you interested? Check out our job board and visit our DeFi Research page for our DeFi analyses.

Disclaimer

This article has been prepared for the general information and understanding of the readers. No representation or warranty, express or implied, is given by Nethermind as to the accuracy or completeness of the information or opinions contained in the above article.

The views and strategies described may not be suitable for all readers. Both past performance and yield may not be a reliable guide to future performance. All case studies are shown for illustrative purposes only and should not be relied upon as advice or interpreted as a recommendation.

The article is based on the scope of materials and documentation publicly available to Nethermind, and it does not indicate Nethermind’s endorsement of any particular asset, project or team, nor guarantee its security. No third party should rely on this article in any way, including for the purpose of making any decisions to buy or sell a product, service or any other asset.

To the fullest extent permitted by law, Nethermind disclaims any liability in connection with this article, its content, and any related services and products and your use thereof, including, without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. Nethermind does not warrant, endorse, guarantee, or assume responsibility for any product or service advertised or offered by a third party through the product, any open source or third-party software, code, libraries, materials, or information linked to, called by, referenced by or accessible through the article, its content, and the related services and products, any hyperlinked websites, any websites or mobile applications appearing on any advertising, and Nethermind will not be a party to or in any way be responsible for monitoring any transaction between you and any third-party. You should use your best judgment and exercise caution where appropriate. FOR AVOIDANCE OF DOUBT, THE ARTICLE, ITS CONTENT, ACCESS, AND/OR USAGE THEREOF, INCLUDING ANY ASSOCIATED SERVICES OR MATERIALS, SHALL NOT BE CONSIDERED OR RELIED UPON AS ANY FORM OF FINANCIAL, INVESTMENT, TAX, LEGAL, REGULATORY, OR OTHER ADVICE.

Will the Shapella Upgrade Unleash Large Waves of Validator Exits? was originally published in Nethermind.eth on Medium, where people are continuing the conversation by highlighting and responding to this story.

By Joel Kahil, Benn Glorfield, and Eli Barbieri.

Executive Summary

The Shanghai upgrade, set to take place in early spring, will permit validators to withdraw their staked ETH, opening the door to new opportunities in the Maximal Extractable Value (MEV) market. In addition, liquid staking tokens, such as stETH, will be redeemable for ETH without the need for a decentralized exchange, enabling a new trading primitive that will allow arbitrage opportunities between liquid staking coins and base ETH. Currently, the potential for MEV profit in liquid staking is limited, and atomic transaction earnings will be low due to decreased market volatility.

Despite this, the adoption of liquid staking tokens is expected to increase in the long term, as they offer superior functionality in various DeFi applications, such as liquidity pools and lending markets. Furthermore, this upgrade will likely drive innovation in the liquid staking market, providing a significant source of MEV and arbitrage opportunities in the future.

Current MEV From Liquid Staking

Liquid staking on Ethereum accounts for a small percentage of total MEV profits. According to data from Flashbots and EigenPhi, liquid staking generated $595k in earnings from the Merge up until January 10th, 2023. In comparison, the total MEV market on Ethereum generated $2.5m in profits during the 30 days leading up to January 18th.

Low DEX Integration: A Headwind For MEV

Atomic (single-transaction) arbitrages account for most of all MEV (upwards of 80%), and roughly 80% of all arbitrages are extracted from either Uniswap V2 or V3. While stETH is the largest liquid staking coin by market capitalization, it is primarily traded on only three DEXes: Curve, Balancer, and Euler Finance. This lack of integration and liquidity leads to fewer arbitrage-based MEV opportunities compared to established Uniswap pairs.

Instead, stETH is primarily used as collateral in lending markets to borrow WETH, and as of January 20th, 2023, 73% of Aave’s stETH supply (673k stETH) was used as collateral to borrow WETH. As a result, the stETH and ETH prices are effectively pegged, making liquid staking tokens behave similarly to stablecoins. This factor, in conjunction with the stability of ETH, makes liquidations of these positions unlikely, further reducing the MEV opportunities compared to pairs like USDC/WETH.

Arbitrage Trades Dominate MEV

Most MEV profits linked to liquid staking came from arbitrage between prices on different platforms, accounting for 80.2% of total profits. Sandwich attacks accounted for 19.1% of MEV profits. Liquidations have generated little MEV since the Merge.

RocketPool’s rETH token appears to be the most arbitraged, but it’s important to note that these rates are highly skewed by one trade executed on November 8th, 2022, during the FTX collapse. This single trade comprised almost half of all extracted MEV from liquid staking tokens at the time of execution. MEV profits are generally not distributed, and a tiny fraction of trades generate outsized profits. In this case, one trade was responsible for $277k out of the $595k total MEV profits from liquid staking. You can find more information about this trade on the EigenPhi Website.

The results are more predictable when looking at the count of MEV transactions between different liquid staking tokens. For example, around 47% of transactions are on stETH, 30% on rETH, and 8% on Coinbase ETH (cbETH). These percentages indicate that while large, outlier trades may skew the overall MEV profits for a specific token, the distribution of transactions across different tokens is more stable.

Short Term Impact

Less MEV Opportunities Due to Lower Price Volatility

When withdrawals are enabled, the discount between liquid staking tokens and ETH will likely narrow significantly, similar to what happened following the Merge. Unlike stablecoins such as USDC and USDT, an exit queue will be in place to manage redemptions. Once a validator has exited the queue, it will have to wait at least 27 hours to withdraw its stake. (More information at https://notes.ethereum.org/@hww/lifecycle#45-Step-5-Unslashed-and-Exited)

New Types of MEV

Currently, there is no way for a searcher to capitalize directly on a trading discount between ETH and a liquid staking token. Any arbitrage that occurs utilizes discounts between different liquid staking tokens. After the Shanghai upgrade, by operating their validators, MEV searchers will be able to implement new strategies, including but not limited to the following:

• If withdrawal demands are too high, validators will want to be at the front of the exit queue to avoid delays. Therefore, it makes sense to pay bribes to ensure that one’s exit request or partial withdrawal is processed.
• Pay validators to back-run users selling a significant quantity of liquid staking tokens.

Arbitrage Opportunities Will Increase

As we have seen previously, withdrawals will take a minimum of 27 hours. As a result, atomic transactions will not be possible; thus, arbitrage opportunities will require capital to be executed. These are not strictly MEV because the transactions are not atomic, and the trade will last as long as the withdrawal duration, creating two classes of liquid staking arbitrage:

1. Buying liquid staking coins when the discount reaches certain levels. When the discount drops below a certain threshold, it’s profitable to arbitrage trades until the withdrawal completes. Even small discounts on liquid staking tokens will be worth arbitraging. For example, if you purchased stETH at a 20 bps discount and redeemed it three days later, you would generate an annualized profit of 23%.
2. Arbitrage between liquid staking tokens. As the liquid staking token market share increases, the additional volatility will create other opportunities. On top of that, staking tokens utilize different rebasing strategies and redemption mechanisms, creating an additional factor when pricing these staking tokens. This market will become similar to stablecoins, where each asset fulfills the same fundamental purpose, but each token has a slightly different implementation.

To make things comparable, we have looked at the historical discount vs. fair value of rETH, cbETH, and stETH. As one can see, the prices of the three tokens are uncorrelated, creating attractive arbitrage opportunities.

Long Term: DeFi Adoption Will Drive MEV Opportunities

The current staking ratio of ETH is low (14%) compared to other chains like Solana (68%) and Polygon (43%). Only when the Shanghai upgrade is implemented are withdrawals possible. Staking entails locking one’s ETH for an undetermined period; this prevents many users from staking their tokens as they may need to access them in the future. Liquid staking tokens provide an alternative, but their discount may widen during volatile periods.

After the upgrade, the liquid staking market will likely expand, and their associated coins will become more critical in the DeFi ecosystem. Liquid staking protocols will have to focus on their decentralization and composability aspects. More DeFi integration combined with higher volumes will increase MEV and arbitrage opportunities from liquid staking coins.

Liquid staking tokens enable composability and better capital efficiency. Liquidity providers will prefer to provide capital to a Uniswap V3 pool USDC/stETH vs. USDC/WETH as they can accrue rewards on their ETH. As liquid staking coins increase ETH staked, they will generate more trading volume. Moreover, protocols will have more incentives to include these coins as collateral in liquidity or even lending pools. For example, cbETH, with a market cap above $1bn as of January 19th, 2023, is an attractive integration candidate.

We also expect to see more L2 adoption in liquid staking, unlocking more cross-chain MEV opportunities. The number of stETH tokens on Ethereum bridges has been increasing steadily since the Merge but remains a small part of the circulating supply (less than 1%).

Disclaimer:

This article has been prepared for the general information and understanding of the readers. No representation or warranty, express or implied, is given by Nethermind as to the accuracy or completeness of the information or opinions contained in the above article.

The article is based on the scope of materials and documentation publicly available to Nethermind, and it does not indicate Nethermind’s endorsement of any particular asset, project or team, nor guarantee its security. No third party should rely on this article in any way, including for the purpose of making any decisions to buy or sell a product, service or any other asset.

Nethermind is a team of world-class builders and researchers. We empower enterprises and developers worldwide to access and build upon the decentralized web. Our work touches every part of the Web3 ecosystem, from our Nethermind node to fundamental cryptography research and application-layer protocol development. We’re always looking for passionate people to join us in solving Ethereum’s most difficult challenges. Are you interested? Check out our job board and visit our DeFi Research page for more analyses of DeFi and Web3.

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Recursive Lending is a very popular strategy in DeFi. It involves lending and borrowing on Compound / AAVE in order to harvest the maximum reward possible. This strategy was very profitable at the beginning of 2021. However, returns have dropped considerably as more money flew into the strategy and the value of the Compound Token fell from $800 on May 21 to $125 at the end of January 22.

In this blog post, we show that given current market conditions, there are simpler, and more rewarding alternatives for DeFi users that are uncorrelated to token prices. One of these alternatives is fixed rate lending on Notional Finance. The latter is even more compelling in the DAI pool, as Compound returns have been historically lower versus USDC. Lending on Notional Finance increases on average your lending returns by 4 to 5% based on current prices.

Recursive Lending in a nutshell

Recursive lending is a strategy on Compound (or Aave) that takes advantage of the fact that the net borrowing rate (after rewards) is higher than the net lending rate. COMP incentives make the borrowing rate lower than the lending rate. For example, as of Feb 2nd, the supply rate is 2.25% and the borrow rate 3.77% p.a for the USDC pool. On top of these yields, lenders will receive 0.93% p.a. in COMP tokens and borrowers 1.38% p.a. Including those incentives, one can borrow USDC at 2.39% and supply it at 3.18% p.a on Compound.

In that configuration, one should deposit stable coins and borrow as much as possible in the same token. Borrowing on Compound needs to be over collateralized so you cannot leverage indefinitely. The platform has a collateral factor (cf) of 80% meaning that if you deposit $100 of collateral, you can only borrow $80 worth of tokens. With a $100 initial lending deposit, one will borrow $80 against it in the same currency. Those $80 will then be re-deposited on Compound and used to borrow $64 (80% of $80) and so on ….

As the collateral ratio is 80%, we can therefore lend $5 with $1 of capital and borrow $4. This enables us to get c10x more COMP reward if we had loaned only $1.

The total return of the strategy equals therefore Lending Rate + 4*(Lending- Borrow Rate) with the lending rate and borrow rate being net of COMP rewards.

People do not borrow 4x times their initial stake as after a certain amount the gas fees become too high versus the interest they expect. Also, they might want to have some margin of safety on our collateral to avoid liquidations. Instadapp and Yearn are using a 4 to 4.6x leverage (i.e. borrow 3x to 3.6x their initial stake instead of 4x)

Historical returns of the Strategy for DAI and USDC

We look at the historical daily APY of the recursive lending strategy since November 1st 2021, which is the launch date of Notional V2. We have assumed a 4.6x leverage in our calculations. The strategy APY has been quite volatile in the last three months and returns have been consistently higher for the USDC pool versus DAI. Since Nov 1st, the annualized return was 6.34% for DAI and 8.88% for USDC.

So far, net supply rates have been above borrow rates for both lending pools, apart from a brief period in December for DAI. The returns have also been declining since November 2021 and the current APY for DAI and USDC (as of Feb 1st) are respectively 5.58% and 4.22%. Those rates are much lower than the current rates one can lock until the end of March on Notional. Notional offers lenders a net return of 9.34% for USDC and 9.25% for DAI. These are 5% superior to the expected returns of a recursive strategy on DAI and c 3.8% higher for USDC.

Main drivers of the recursive strategy

As we have seen previously, the returns of a recursive strategy are quite volatile through time. They will depend on 1) the value of COMP token, 2) the utilization rate of the pool, 3) how much AUM in the pool is sharing those rewards 4) ethereum prices.

1. COMP token prices

A recursive lending strategy is subsidized by the COMP rewards a user will receive for borrowing and lending. With supply rates before rewards being close to 2%, COMP rewards represent between 50% and ⅔ of the total returns. Most of the volatility of the strategy comes from the COMP token volatility.

As you can see below, the strategy APYs are extremely correlated to the COMP token price.

As a result, market neutral users will try to lock in returns by hedging in advance the amount of COMP token they expect to receive using futures on platforms like Binance or Perpetual Protocols. With hindsight, it would have been the best strategy to adopt early November 2021 when the COMP token was close to $400. However, hedging at current rates will guarantee you suboptimal returns between 4 and 5% annualized. Using Notional fixed rates looks like a much more attractive alternative.

If the rewards are left unhedged, your returns can be wiped out by a sharp decline in COMP prices, like the one we have experienced in the last three months.

We have plotted below the return achieved by the strategies for various COMP price scenarios.

To achieve Notional’s 9.3% annualized return, the recursive strategy needs COMP token to be worth $170 for USDC and $185 for DAI vs a current price as of time of writing of $125. But COMP prices cut both ways. If they were to fall to $50, returns would fall to 0.

If you want to keep some exposure to COMP, one interesting strategy for lenders would be to lend on Notional and buy some COMP tokens upfront for the amount of interest you will receive. This way you will be guaranteed to outperform a recursive strategy whatever the future COMP price.

In the example below, we have lent our USDC for a 9.3% net yield, we then buy 9.3% of COMP Tokens. If the COMP token price doubles you will have made a profit of 18.6% over one year but your profit goes to 0 if COMP drops to 0. This strategy always outperforms a recursive strategy even if COMP goes to $500 !!

2. Utilization Rates

Interest rates on Compound are variable and they are a function of the utilization rate of the pool. If you want to go into the nitty-gritty of the interest rate model, I highly recommend Ian Macalinao’s article: https://ian.pw/posts/2020-12-20-understanding-compound-protocols-interest-rates

The short version is that rates increase slowly with utilization until we reach a pool optimal utilization rate (80% for USDC and DAI). They accelerate when we go past 80% to urge borrowers to supply money on the platform and lenders to reduce borrowing.

The interest rate before COMP rewards takes the following shape:

If we factor the COMP rewards into our equation, as utilization rates drop (i.e less people borrowing with a constant amount of lending), COMP rewards for borrowers are fixed (440.19 COMP token every day) shared by a smaller amount of borrowers hence they increase as a % of funds. This makes the recursive lending strategy even more profitable and offsets the reduction in lending rate.

Given the current price of the COMP token, and all things staying equal, we can compute the return of the recursive lending strategy for DAI and USDC as a function of utilization rate.

The recursive strategy is very profitable when there is little borrowing on the platform (situation similar to Spring 2021) and returns are minimal around 60% utilization rate to go up slowly until 85% utilization. Above 80% utilization rate, the spread between lending and borrowing turns negative and the recursive strategy will underperform a standard unlevered lending strategy.

As we can see on these charts, for the recursive strategy to reach the level of yield offered by Notional (c10%), you either need the utilization rate to drop consistently below 30% or go consistently above 85%, both seem very unlikely in the short term.

3. AUM on the platform

As we mentioned before, the amount of COMP reward distributed is fixed (880.38 COMP tokens for each pool split evenly between borrowers and lenders). The TVL in the pool is unlikely to stay constant. As DeFi continues to attract capital, TVL in Compound is bound to rise and dilute the rewards investors will receive. A 10% increase in TVL has the same impact as a 10% drop in COMP price on the rewards distributed. This gives another strong case for fixing your lending interest rates.

4. Ethereum prices

Ethereum prices have an indirect impact on interest rates. People usually borrow stable coins and use ETH as a collateral (when they are not doing recursive lending).

An increase in ETH prices leads to more borrowing on the platform and inversely. ETH prices have a counterbalancing effect on strategy returns: when they decrease, TVL drops, leading to higher rewards for users.

According to the below graph, the total DAI amount borrowed has been strongly correlated to Ethereum prices.

How likely are Notional returns to beat the returns of a recursive strategy?

We are ending this article by computing the probability that Notional’s fixed interest rate outperforms a recursive lending strategy over the next 2 months (expiry of the short term contract). We run price simulations on COMP token price, the pool utilization rate and the TVL of the pool. We have analyzed historic data on Compound to come up with our modeling assumptions.

COMP prices are modeled using a Student’s t-distribution (5 degrees of freedom and an annualized volatility of 130% p.a.). We assume changes in the pool utilization rate follow a normal distribution with a standard deviation of 1.5% per day. We make the TVL on the lending pool dependent on the return produced by the strategy. This makes the simulation realistic by including a stabilization mechanism: high returns generate inflows on Compound and low returns outflows. As returns drop to 4%, we assume that users reduce their leverage and the expected return goes back up so there is intrinsically a floor to returns. Also historically, utilization rates have rarely gone above 90% as there are mechanisms in place in the protocol to rein back borrowing when we reach this level.

Based on current market conditions, a Notional Fixed rate is expected to outperform between 80% and 85% of the time. The actual number depends on the modeling assumptions used for COMP volatility and users’ behaviors.

A flurry of investment strategies have recently emerged on Uniswap V3 to improve yield farming returns. The most notable include the auto rebalancing of liquidity positions. Many investors are attracted to the idea of using an auto rebalancing strategy on their LP in order to keep in their positions inside the liquidity range and maximize the fees they receive.

However, such strategies increase complexity, by introducing path dependence as well as additional gas fees and operational risk. Hence they have been met by incomprehension and caution by some members of the DeFi community.

In this paper, we present an evaluation of rebalancing strategies for Uniswap V3, a decentralized token exchange protocol built on Ethereum. We first go through the mechanics of rebalancing and highlight the main risk exposures added versus an equivalent passive strategy. In the second part of this paper, we run Monte Carlo simulations to model returns and understand which environment is the most conducive to rebalancing. We conclude that:

• Auto rebalancing was attractive when Uniswap v3 launched in May when fees APR were high. Since then, volumes on most pools have plummeted and no longer compensate for the extra volatility risk.
• Auto rebalancing strategies suffer a volatility drag versus passive strategies as they force investors to crystallize their impermanent losses. They increase investors gamma exposure versus passive. These strategies need a high level of fees to be profitable. Therefore one should turn on rebalancing only during periods of high volumes.
• As long as the price remain within our initial liquidity range, we are still collecting the same amount of fees. So there is little incentive in rebalancing our positions. We will significantly increase our gamma risk without a meaningful increase in fee collection.
• Liquidity providers should choose their LP managers with extra care as the selection of the rebalancing range and frequency are significant drivers of returns. Investors should only choose pairs with an attractive fee to token volatility ratio.

Preliminary Remarks

In this paper we model a rebalancing mechanism close to the Alpha Vault proposed by Charm Finance. Please note that our model will defer versus what is actually implemented but the purpose of this article is not to judge the Alpha Vault Strategy but use a concrete example for our analysis. We will benchmark all our results versus 100% USDC and not 50/50 as it is often used by many DeFi providers. our aim being to make an absolute return vs USDC or fiat.

Principles of auto rebalancing

In a passive LP strategy, the investor will usually set up a liquidity range [Pmin,Pmax] and do nothing until liquidity is withdrawn. The profit of a standard LP is a function of the terminal price and the amount of fees collected. The amount of fees you will receive will depend on the % of time the token price has spent in the liquidity range (and also the width of the range).

Through time, it is not uncommon to have a price drift i.e. the token price has moved out of your initial liquidity range, meaning that you have stopped earning fees. So at first sight it would make sense to rebalance your position to avoid that drift ad continue collecting more fees.

The Alpha Vault Concept

The concept of the Alpha Vault has been documented here by Max https://medium.com/charmfinance/introducing-alpha-vaults-an-lp-strategy-for-uniswap-v3-ebf500b67796

The idea is to choose a base liquidity range symmetric around the current price [Current -B, Current+B] and to rebalance regularly (let’s assume twice a day). As the token price moves, our initial ratio (50/50 each asset) will drift. As our token price decreases, we buy more of the token and inversely as it increases.

Now let’s assume that the price moves to X and that our portfolio is now composed of 70% T and 30% USDC. The strategy will post a new LP position at [X-B, X+B] for 30%USDC/30% T. The remaining extra 40% T is placed as a passive rebalancing order at [X,X+R] with R being the rebalancing range. At every rebalancing, we count the number of token we hold and redo the same process.

Increased fees for increased Volatility exposure

Let’s put that into practice with a concrete example.

Let’s assume we start T/USDC=100, we put our liquidity inside the range [90..110] and the price at the rebalancing date is 105. Our portfolio is now composed of 163.3 USDC and 0.4821 T. Expressed in USDC, our portfolio is now 76.3% USDC and 23.7% T. So we have an extra 52.6% of USDC which will translate into a buy order of Token T at a range [104,105]( range is just an illustration). The rest of the position will be placed in a 23.7%/23.7% range[95,115].

Below is the comparison between active and passive strategies depending on what is the next price of Token T.

We can see that the impermanent loss of the Rebalance Strategy will underperform a passive strategy most of the time except in the case where Token T continues to appreciate (in our case finishes above 105). Versus a passive Strategy, the rebalancing strategy is short realized volatility and is expected to produce a higher impermanent loss. However, this strategy will have more concentrated liquidity giving us the opportunity to earn more fees.

Simulating Returns

As we can see the returns of a rebalancing strategy depend on the level of realized return volatility. The strategy is expected to outperform in a trending market but underperform in a choppy market.

In our previous article https://medium.com/@DeFiScientist/uniswap-v3-a-quant-framework-to-model-yield-farming-returns-941a1600425e, we concluded that the level of fees had to be high enough to compensate for the volatility risk taken by liquidity providers. We found out that on most pools the fees were too low for yield farming to be a viable strategy versus selling puts.

In the case of an auto rebalancing strategy, it seems that the level of fees relative to volatility is even more important.

Assumptions

We are going to explore the sensitivity of the relative returns versus passive with Monte Carlo simulations. These are more robust than the historical backtesting most people do provided you have the correct initial assumptions. Here are our base assumptions:

• Log returns follow a t student distribution wish degree of freedom 5. This looks to fit past returns for Bitcoin and Ethereum. A t student distribution is more realistic than a normal distribution as it allows for fat tails. Remember the BTC flash crash of May 2021 when BTC collapsed 25% in 1 hour this would have never happened in a normal distribution.
• Our simulations do not assume any prior knowledge of future returns on the pair. If we knew this, we would not use LP!
• We simulate returns every 6 hours this enables us to get a better modelling of the amount of fees received during the period.
• Rebalancing every 12 hours and simulate returns over 5 days (20 time steps)
• For simplicity, we assume flat liquidity all over the price range. We know that this is not the case but we do not want to incorporate additional complexity at this stage.
• Pair used : WETH/USDC (fees 0.3%)
• Implied volatility assumed 110% in line with option markets.
• Pool Fees: 400 000$ a day. Many of the simulations you see from providers are based on past data where fees and volumes were much higher. For example volumes were 7x higher in May than currently with a TVL only 50% higher on WETH/USDC. Their results will show very good results but we believe those results will not be replicated in the future due to increased competition.
• Base range +-9.6% for both strategies. Rebalancing range:+-4.8%

Sensitivity to Base range

In this simulation, we look at various base ranges but fix the rebalancing range at 4.8%. Both passive and rebalancing strategies have the same base range for like for like comparisons. The auto rebalancing strategy will underperform on average with the underperformance being particularly acute for small ranges. The extra fees we collect are not enough to compensate for our short volatility position.

In auto rebalancing strategies, the range should not be too narrow as every time we rebalance we crystallize our losses and risk getting consistently whipsawed.

Sensitivity to Rebalancing range

We fixed the base range at 9.6% and used various rebalancing ranges. From our simulations, there seems to be an inverse linear relationship between performance and the size of the range.=> the smallest the range the better.

Sensitivity to Rebalancing Frequency

In those simulations we have kept the rebalancing and base range constant and we use various ranges of rebalancing frequency (expressed as multiples of 6 hours). We also use two types of volatility regimes (high vol: 110% and low vol : 50%).

If we rebalance too frequently we are going to get whipsawed too often and our performance will materially suffer. The rebalancing strategy can add value provided 1) the level of volatility is low enough relative to the fees we collect 2) the rebalancing frequency is appropriate.

Conclusion

Fixed rebalancing parameters do not seem ideal. As rebalancing is a costly process, it should only be done when necessary. We also believe that the ideal solution lies in using dynamic parameters. In the upcoming weeks, we plan to carry out further research in that direction. I hope that you enjoyed our second analysis of Uniswap V3. We have more innovative quant research in the pipeline so do not forget to follow us on Medium and Twitter https://twitter.com/DefiScientist

Disclaimer: The content of this post is provided for informational purposes only. Nothing herein constitutes investment, legal, or tax advice or recommendations. Nothing on this site should not be relied upon as a basis for making an investment decision. It should not be assumed that any investment in the asset class described herein will be profitable and there can be no assurance that future events and market factors would lead to results similar to any historical results described.

Uniswap v3 launched officially in May promising lower transaction fees and better capital efficiency for liquidity pool (LP) providers. The DeFi protocol quickly witnessed tremendous success attracting close to $2bn capital within its first month.

However, returns for Liquidity Providers have been mixed since the launch. Based on current platform metrics, we demonstrate that there is too much liquidity on the platform chasing too few fees. As a result, for most pairs, the fees generated by the LP, far from compensate the expected impermanent loss (IPL) arising from the volatility in token prices. Given current market conditions, investors can expected a higher return from selling puts (when available).

To demonstrate this, we first expose the main drivers of returns of yield farming on Uniswap V3 and propose a quant framework to analyse the attractiveness of a given LP position. As an illustration, we apply this framework to the USDC/WETH pool.

Unlike most currently available methods, our framework is unique as it takes into account the liquidity distribution of the LP and simulates associated token price movements. This enables a more precise estimation of the fees one can expect to collect and subsequently of the expected strategy profit.

Yield Farming: back to basics

Before we dive into the quant stuff, let’s remind ourselves of the features of the Uniswap V3 protocol and the drivers of yield farming returns.

It is worth noting that any LP strategy is a short volatility strategy: you contribute two tokens into the liquidity pool and as the price moves, you sell progressively the best performing asset.

The main innovation of Uniswap V3 lies in:

• the ability to concentrate liquidity within a specific price range [pmin, pmax]. This provides leverage to the LP. But as soon as the prices settles outside of the range, you will stop earning fees and will have full exposure to the underperforming asset.
• the fees you will collect depend on how many other LPs are active in a given price range. Those fees are harder to model than for Uniswap V2 as you are in competition with other liquidity providers.

Let’s model the expected impermanent loss for a pool where we trade a crypto token (let’s call it T) versus a stable coin (i.e. USDC). We start with a portfolio value of 1 and a market price of T/USDC =1) What does our Portfolio value looks like (excluding fees) for various values of USDC/T?

We also compare this for two different liquidity ranges [0.95,1.05] and [0.9,1.1], so readers can get a better understanding for the impact of the liquidity range on the outcome.

As one can see, the PL graph is very similar to the graph of a short put strategy. The main difference being in the convexity within the chosen liquidity range. A narrower liquidity range will help you collect more fees — as long as the token price remains in the range- but you will have less upside exposure to the crypto token and more downside exposure.

Your main risk is in your token depreciating and in a crypto bull market you make money from your short volatility position and from the pool fees. However, the payoff in USD without fees is asymmetric. a small gain (c 50% of the time) when the token appreciates vs USD but a potential large loss on the downside. This leads to an average statistical loss. The LP strategy is only viable if the fees collected are bigger than on your expected IPL.

Setting up correctly your Liquidity Range is a critical part of any LP strategy. The fees that you will collect are also path dependant. To model our returns precisely, we are going to simulate token prices with Monte Carlo simulations.

.A Quant Framework for Modelling of LP returns

Given a certain token LP position and its characteristics (range, liquidity distribution of the pool and fee forecast), one can calculate an implied volatility metric assumed by the LP position.

The implied volatility metric is backed out using Monte Carlo simulations of the token pair price. The token volatility implied by the pool being the asset volatility that generates a 0 expected gain in the LP position.

Worth noting that we could reverse the process i.e. set our implied asset volatility assumptions for the pair and back out the implied fees.

As the liquidity distribution in a pool can change over time, it makes sense to run Monte Carlo simulations over a short time period. We will select a 24 hour window for the purpose of this modelling exercise.

Application to the USDC/WETH Pool

USDC/WETH is one of the largest pool in Uniswap V3 with more $200m in TVL. We start by extracting the liquidity distribution using The Graph . We notice that the bulk of the liquidity is concentrated within the 1800–2400 price range for WETH. The liquidity starts degrading slowly as the price falls from current levels. We should expect to collect more fees as the price falls further-as we will account for a higher part of the pool. As WETH appreciates, the fees we collect will diminish.

We also need to set up a daily fee assumption for the pool. We can see below that daily volumes have been recently well below $200m. Let’s be optimistic and assume a $200m daily volume which should translate into 600k fees ( 0.3% transaction fee) for the entire liquidity pool. Given a TVL of $235m, this would represent an APY of 93% p.a.

Using the framework described previously and our fee assumptions, we will calculate the implied volatility for 3 different liquidity ranges:

• ± 1.2 % range from the current price (1760 at the time of writing)
• ± 4.8 % range from the current price
• ± 9.6 % range from the current price

Our framework is extremely flexible and could accommodate any liquidity range. These three ranges have been chosen to illustrate that the implied volatility does not change materially with the liquidity range.

The implied volatility for ± 1.2 % range is 99% implied volatility.

The implied volatility for ± 4.8 % range is 100.5% implied volatility.

The implied volatility for ± 9.6 % range is 107% implied volatility.

Even using our optimistic assumptions on fees, the implied volatility of those three pool positions is below the 122% level on ETH put options (source Deribit Exchange). In practice, the fees are expected to be much lower ( they were c 400–450k daily at the time of writing!) leading to a much lower implied volatility. One also could argue that implied volatility curves have skews. Therefore a wider liquidity range should justify a higher implied volatility, making LP investments even less attractive. For information, the 1650 daily Put Deribit Exchange was pricing a 152% implied volatility.

Based on a 120% implied volatility and 600k daily fees, we can expect the following daily losses on our three pools:

• ± 1.2 % range : -1.1% If the fees are too low, a narrow liquidity range will hurt you like leveraging a bad trade.
• ± 4.8 % range : -0.83%
• ± 9.6 % range : -0.5%

Now, let’s turn the problem around, let’s assume an implied volatility of 120% and let’s figure out the daily fees required to breakeven. The ± 9.6 % range pool needs a 800k daily volume (124% APY) to have an expected positive return which is 2x current volumes. Or alternatively, we would need the liquidity being provided to be halved.

We have seen that the USDC/WETH has too much capital and the volumes are not enough to compensate for the implied volatility of ETH. But is it the same for all the pairs available on Uniswap ?

Screening for attractive LPs

The best way to screen quickly for attractive pools is to calculate the ratio between expected fees and realized volatility of the pair token. That is where The Graph becomes handy again to pull out all the pools data. A good rule of thumb is that fees need to have an APY at least 0.9x the implied volatility of the pair we are providing liquidity for. This could differ if for example, we have a good portion of the TVL which does not provide liquidity at current market prices. In our example of the USDC/WETH, the bulk of the liquidity was concentrated above current market prices so the pool APY was underestimating the fees we could earn.

The chart below breaks down Uniswap TVL by the APY/realized volatility ratio for the various pools. One can see that c 2/3 of he TVL is locked in pools where the expected APY is less than the implied pair volatility ! Only 1/3 of money locked has an expected positive return.

The top 20 best pools based on the fees/vol ratio are shown below. As you can see those pools are quite small in size with at max a few millions dollars of assets. Their returns will be impacted by higher gas fees and the risk of new entrants in the Pool. Also, many of these involve small , less reputable coins like FEI,FRAX …

You will also notice a USDC/WETH pool (highlighted in yellow) in the list. (https://info.uniswap.org/#/pools/0x7bea39867e4169dbe237d55c8242a8f2fcdcc387) That is not the pool we analysed previously but the one with the 1% transaction fee (!) . Its TVL is much smaller ($2.7m). This proves that the standard Uniswap fee is not always the most optimal for yield farming.

Conclusion

Uniswap is a victim of its success. As Luke Posey and glassnode pointed out in their recent newsletter (https://insights.glassnode.com/activity-on-defi-stalls/), the TVL locked on DeFI platform has outperformed the performance of Ethereum and other crypto tokens.

This creates more competition for fees from liquidity providers. In addition, the proliferation of exchanges and the new Uniswap fee tiers have had a negative impact on fees and in return the profitability of yield farming strategies.

I hope that you enjoyed our first analysis of Uniswap V3. We have more innovative quant research in the pipeline so do not forget to follow us on Medium and Twitter https://twitter.com/DefiScientist